What is statistical learning?

Functional form

\[Y = f(X) + \epsilon\]

• Statistical learning refers to the set of approaches for estimating \(f\)

Linear functional form

Why estimate \(f\)?

• Prediction
• Inference
• How do we estimate \(f\)?
  • Parametric methods
  • Non-parametric methods

Parametric methods

1. First make an assumption about the functional form of \(f\)
2. After a model has been selected, fit or train the model using the actual data

OLS

Parametric methods

\[Y = \beta_0 + \beta_{1}X_1\]

• \(Y =\) sales
• \(X_{1} =\) advertising spending in a given medium
• \(\beta_0 =\) intercept
• \(\beta_1 =\) slope

Non-parametric methods

• No assumptions about functional form
• Use data to estimate \(f\) directly
  • Get close to data points
  • Avoid overcomplexity
• Requires large amount of observations

LOESS

Types of learning

• Supervised
• Unsupervised

Statistical learning vs. machine learning

• Statistical learning
  • Subfield of statistics
  • Focused predominantly on inference
• Machine learning
  • Subfield of computer science
  • Focused predominantly on prediction

Why R?

Popularity

Why R?

Why R?

Things R does well

• Statistical analysis
• Data visualization

Things R does not do as well

• Speed

Why are we not using Python?

Resources for learning R

• R for Data Science
• An Introduction to Statistical Learning
• Computing for the Social Sciences
• RStudio Cheat Sheets

But I don’t wanna!

Caveat emptor

• Python Data Science Handbook
• Google
• StackOverflow

Acknowledgments

• This presentation is derived in part from “Creating a LOESS animation with gganimate” by David Robinson.
• This presentation is derived in part from “Machine Learning vs. Statistical Modeling: The Real Difference”
• For more information on statistical learning and the math behind these methods, see the awesome book An Introduction to Statistical Learning